An ontology is understood as a formal specification of terminology and concepts, as well as the relationships among those concepts, relevant to a particular domain or area of interest. Ontologies provide insight into the nature of information particular to a given field and are essential to any attempts to arrive at a shared understanding of the relevant concepts. They may be specified at various levels of complexity and formality depending on the domain and information needs of the participants in a given conversation.
For classical problem solving tasks, often, there is a need to use the knowledge from different multiple knowledge repositories and ontologies. This is particularly the case in the context of knowledge-rich working tasks requiring the integration of complementary knowledge from different sources and domains. One prominent example is medical imaging, where a single ontology is not enough to provide the complementary knowledge about anatomy, radiology and diseases that is required by the related applications.
Consequently, an integration of different but related types of knowledge, provisioned in disparate domain ontologies, becomes necessary.
Currently known approaches in the field of ontology alignment address this need by identifying equivalent concepts across multiple ontologies. Ontology alignment, also referred to as ontology matching or ontology mapping, is the process of determining correspondences between related or equal concepts of disparate ontologies. These concepts are then made compatible with each other through meaningful relationships.
Ontology alignment may be also understood as a special case of semantic integration that concerns a semi-automatic discovery of semantically or otherwise related concepts across two or more ontologies.
An ontology alignment may be used to align medical images with related patient text data. Any kind of application operating on these alignments is capable of delivering a coherent set of available information by contrast to solely parsing of simple keywords.
Currently known ontology alignment tools provide a development of scalable methods by combining string-based methods with complex structural methods, including tools for supporting users to tackle an interoperability problem between distributed knowledge sources. Latter tools include editors for iterative, semi-automatic mapping with incremental visualizations. However, the usage of complex methods for ontology alignment turns out to be unfeasible, particularly in the medical domain. This is mainly due to a size of a concept and relation matrix which frequently reaches a size of 100,000×100,000 alignment cells. In parallel, a creation of appropriate sub-ontologies is not possible because of complex inter-dependencies.
An alternative approach known in the art follows a pragmatic method for handling the complexity of ontologies within the medical domain. According to this approach, an information retrieval is applied in order to discover relationships between a first ontology and a second ontology by applying an indexed ontology concept to the first ontology and by matching the relationships against search queries being concepts of the second ontology. Although this approach is rather efficient and easy to implement, it does not account for the complex linguistic structure typically observed in the concept labels of medical ontologies and may also result in inaccurate matches.